Merge pull request #7 from Cadene/user/rcadene/2024_03_05_abstract_replay_buffer

Add AbstractReplayBuffer

lerobot/common/datasets/abstract.py

@@ -0,0 +1,158 @@
+import abc
+import logging
+from pathlib import Path
+from typing import Callable
+
+import einops
+import torch
+import torchrl
+import tqdm
+from tensordict import TensorDict
+from torchrl.data.datasets.utils import _get_root_dir
+from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
+from torchrl.data.replay_buffers.samplers import SliceSampler
+from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
+from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
+
+
+class AbstractExperienceReplay(TensorDictReplayBuffer):
+    def __init__(
+        self,
+        dataset_id: str,
+        batch_size: int = None,
+        *,
+        shuffle: bool = True,
+        root: Path = None,
+        pin_memory: bool = False,
+        prefetch: int = None,
+        sampler: SliceSampler = None,
+        collate_fn: Callable = None,
+        writer: Writer = None,
+        transform: "torchrl.envs.Transform" = None,
+    ):
+        self.dataset_id = dataset_id
+        self.shuffle = shuffle
+        self.root = _get_root_dir(self.dataset_id) if root is None else root
+        self.root = Path(self.root)
+        self.data_dir = self.root / self.dataset_id
+
+        storage = self._download_or_load_storage()
+
+        super().__init__(
+            storage=storage,
+            sampler=sampler,
+            writer=ImmutableDatasetWriter() if writer is None else writer,
+            collate_fn=_collate_id if collate_fn is None else collate_fn,
+            pin_memory=pin_memory,
+            prefetch=prefetch,
+            batch_size=batch_size,
+            transform=transform,
+        )
+
+    @property
+    def stats_patterns(self) -> dict:
+        return {
+            ("observation", "state"): "b c -> 1 c",
+            ("observation", "image"): "b c h w -> 1 c 1 1",
+            ("action"): "b c -> 1 c",
+        }
+
+    @property
+    def image_keys(self) -> list:
+        return [("observation", "image")]
+
+    @property
+    def num_cameras(self) -> int:
+        return len(self.image_keys)
+
+    @property
+    def num_samples(self) -> int:
+        return len(self)
+
+    @property
+    def num_episodes(self) -> int:
+        return len(self._storage._storage["episode"].unique())
+
+    def set_transform(self, transform):
+        self.transform = transform
+
+    def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
+        stats_path = self.data_dir / "stats.pth"
+        if stats_path.exists():
+            stats = torch.load(stats_path)
+        else:
+            logging.info(f"compute_stats and save to {stats_path}")
+            stats = self._compute_stats(num_batch, batch_size)
+            torch.save(stats, stats_path)
+        return stats
+
+    @abc.abstractmethod
+    def _download_and_preproc(self) -> torch.StorageBase:
+        raise NotImplementedError()
+
+    def _download_or_load_storage(self):
+        if not self._is_downloaded():
+            storage = self._download_and_preproc()
+        else:
+            storage = TensorStorage(TensorDict.load_memmap(self.data_dir))
+        return storage
+
+    def _is_downloaded(self) -> bool:
+        return self.data_dir.is_dir()
+
+    def _compute_stats(self, num_batch=100, batch_size=32):
+        rb = TensorDictReplayBuffer(
+            storage=self._storage,
+            batch_size=batch_size,
+            prefetch=True,
+        )
+
+        mean, std, max, min = {}, {}, {}, {}
+
+        # compute mean, min, max
+        for _ in tqdm.tqdm(range(num_batch)):
+            batch = rb.sample()
+            for key, pattern in self.stats_patterns.items():
+                batch[key] = batch[key].float()
+                if key not in mean:
+                    # first batch initialize mean, min, max
+                    mean[key] = einops.reduce(batch[key], pattern, "mean")
+                    max[key] = einops.reduce(batch[key], pattern, "max")
+                    min[key] = einops.reduce(batch[key], pattern, "min")
+                else:
+                    mean[key] += einops.reduce(batch[key], pattern, "mean")
+                    max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
+                    min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
+                batch = rb.sample()
+
+        for key in self.stats_patterns:
+            mean[key] /= num_batch
+
+        # compute std, min, max
+        for _ in tqdm.tqdm(range(num_batch)):
+            batch = rb.sample()
+            for key, pattern in self.stats_patterns.items():
+                batch[key] = batch[key].float()
+                batch_mean = einops.reduce(batch[key], pattern, "mean")
+                if key not in std:
+                    # first batch initialize std
+                    std[key] = (batch_mean - mean[key]) ** 2
+                else:
+                    std[key] += (batch_mean - mean[key]) ** 2
+                max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
+                min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
+
+        for key in self.stats_patterns:
+            std[key] = torch.sqrt(std[key] / num_batch)
+
+        stats = TensorDict({}, batch_size=[])
+        for key in self.stats_patterns:
+            stats[(*key, "mean")] = mean[key]
+            stats[(*key, "std")] = std[key]
+            stats[(*key, "max")] = max[key]
+            stats[(*key, "min")] = min[key]
+
+            if key[0] == "observation":
+                # use same stats for the next observations
+                stats[("next", *key)] = stats[key]
+        return stats

lerobot/common/datasets/factory.py

@@ -5,33 +5,14 @@ from pathlib import Path
 import torch
 from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
 
-from lerobot.common.datasets.pusht import PushtExperienceReplay
+from lerobot.common.envs.transforms import NormalizeTransform
-from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
 
 DATA_DIR = Path(os.environ.get("DATA_DIR", "data"))
 
-# TODO(rcadene): implement
 
-# dataset_d4rl = D4RLExperienceReplay(
+def make_offline_buffer(
-#     dataset_id="maze2d-umaze-v1",
+    cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
-#     split_trajs=False,
+):
-#     batch_size=1,
-#     sampler=SamplerWithoutReplacement(drop_last=False),
-#     prefetch=4,
-#     direct_download=True,
-# )
-
-# dataset_openx = OpenXExperienceReplay(
-#     "cmu_stretch",
-#     batch_size=1,
-#     num_slices=1,
-#     #download="force",
-#     streaming=False,
-#     root="data",
-# )
-
-
-def make_offline_buffer(cfg, sampler=None):
     if cfg.policy.balanced_sampling:
         assert cfg.online_steps > 0
         batch_size = None
@@ -44,9 +25,13 @@ def make_offline_buffer(cfg, sampler=None):
         pin_memory = cfg.device == "cuda"
         prefetch = cfg.prefetch
 
-    overwrite_sampler = sampler is not None
+    if overwrite_batch_size is not None:
+        batch_size = overwrite_batch_size
 
-    if not overwrite_sampler:
+    if overwrite_prefetch is not None:
+        prefetch = overwrite_prefetch
+
+    if overwrite_sampler is None:
         # TODO(rcadene): move batch_size outside
         num_traj_per_batch = cfg.policy.batch_size  # // cfg.horizon
         # TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
@@ -67,36 +52,57 @@ def make_offline_buffer(cfg, sampler=None):
                 num_slices=num_traj_per_batch,
                 strict_length=False,
             )
+    else:
+        sampler = overwrite_sampler
 
     if cfg.env.name == "simxarm":
-        # TODO(rcadene): add PrioritizedSliceSampler inside Simxarm to not have to `sampler.extend(index)` here
+        from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
-        offline_buffer = SimxarmExperienceReplay(
+
-            f"xarm_{cfg.env.task}_medium",
+        clsfunc = SimxarmExperienceReplay
-            # download="force",
+        dataset_id = f"xarm_{cfg.env.task}_medium"
-            download=True,
+
-            streaming=False,
-            root=str(DATA_DIR),
-            sampler=sampler,
-            batch_size=batch_size,
-            pin_memory=pin_memory,
-            prefetch=prefetch if isinstance(prefetch, int) else None,
-        )
     elif cfg.env.name == "pusht":
-        offline_buffer = PushtExperienceReplay(
+        from lerobot.common.datasets.pusht import PushtExperienceReplay
-            "pusht",
+
-            streaming=False,
+        clsfunc = PushtExperienceReplay
-            root=DATA_DIR,
+        dataset_id = "pusht"
-            sampler=sampler,
-            batch_size=batch_size,
-            pin_memory=pin_memory,
-            prefetch=prefetch if isinstance(prefetch, int) else None,
-        )
     else:
         raise ValueError(cfg.env.name)
 
+    offline_buffer = clsfunc(
+        dataset_id=dataset_id,
+        root=DATA_DIR,
+        sampler=sampler,
+        batch_size=batch_size,
+        pin_memory=pin_memory,
+        prefetch=prefetch if isinstance(prefetch, int) else None,
+    )
+
+    if normalize:
+        # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
+        stats = offline_buffer.compute_or_load_stats()
+        in_keys = [("observation", "state"), ("action")]
+
+        if cfg.policy == "tdmpc":
+            for key in offline_buffer.image_keys:
+                # TODO(rcadene): imagenet normalization is applied inside diffusion policy, but no normalization inside tdmpc
+                in_keys.append(key)
+                # since we use next observations in tdmpc
+                in_keys.append(("next", *key))
+            in_keys.append(("next", "observation", "state"))
+
+        if cfg.policy == "diffusion" and cfg.env.name == "pusht":
+            # TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
+            stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
+            stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
+            stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
+            stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
+
+        transform = NormalizeTransform(stats, in_keys, mode="min_max")
+        offline_buffer.set_transform(transform)
+
     if not overwrite_sampler:
-        num_steps = len(offline_buffer)
+        index = torch.arange(0, offline_buffer.num_samples, 1)
-        index = torch.arange(0, num_steps, 1)
         sampler.extend(index)
 
     return offline_buffer

lerobot/common/datasets/pusht.py

@@ -1,6 +1,3 @@
-import logging
-import math
-import os
 from pathlib import Path
 from typing import Callable
 
@@ -12,16 +9,14 @@ import torch
 import torchrl
 import tqdm
 from tensordict import TensorDict
-from torchrl.data.datasets.utils import _get_root_dir
+from torchrl.data.replay_buffers.samplers import SliceSampler
-from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
+from torchrl.data.replay_buffers.storages import TensorStorage
-from torchrl.data.replay_buffers.samplers import Sampler
+from torchrl.data.replay_buffers.writers import Writer
-from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
-from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
 
 from diffusion_policy.common.replay_buffer import ReplayBuffer as DiffusionPolicyReplayBuffer
 from diffusion_policy.env.pusht.pusht_env import pymunk_to_shapely
+from lerobot.common.datasets.abstract import AbstractExperienceReplay
 from lerobot.common.datasets.utils import download_and_extract_zip
-from lerobot.common.envs.transforms import NormalizeTransform
 
 # as define in env
 SUCCESS_THRESHOLD = 0.95  # 95% coverage,
@@ -87,114 +82,36 @@ def add_tee(
     return body
 
 
-class PushtExperienceReplay(TensorDictReplayBuffer):
+class PushtExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
         dataset_id: str,
         batch_size: int = None,
         *,
         shuffle: bool = True,
-        num_slices: int = None,
-        slice_len: int = None,
-        pad: float = None,
-        replacement: bool = None,
-        streaming: bool = False,
         root: Path = None,
-        sampler: Sampler = None,
-        writer: Writer = None,
-        collate_fn: Callable = None,
         pin_memory: bool = False,
         prefetch: int = None,
-        transform: "torchrl.envs.Transform" = None,  # noqa: F821
+        sampler: SliceSampler = None,
-        split_trajs: bool = False,
+        collate_fn: Callable = None,
-        strict_length: bool = True,
+        writer: Writer = None,
+        transform: "torchrl.envs.Transform" = None,
     ):
-        if streaming:
-            raise NotImplementedError
-        self.streaming = streaming
-        self.dataset_id = dataset_id
-        self.split_trajs = split_trajs
-        self.shuffle = shuffle
-        self.num_slices = num_slices
-        self.slice_len = slice_len
-        self.pad = pad
-
-        self.strict_length = strict_length
-        if (self.num_slices is not None) and (self.slice_len is not None):
-            raise ValueError("num_slices or slice_len can be not None, but not both.")
-        if split_trajs:
-            raise NotImplementedError
-
-        if root is None:
-            root = _get_root_dir("pusht")
-            os.makedirs(root, exist_ok=True)
-
-        self.root = root
-        if not self._is_downloaded():
-            storage = self._download_and_preproc()
-        else:
-            storage = TensorStorage(TensorDict.load_memmap(self.root / dataset_id))
-
-        stats = self._compute_or_load_stats(storage)
-        transform = NormalizeTransform(
-            stats,
-            in_keys=[
-                # TODO(rcadene): imagenet normalization is applied inside diffusion policy
-                # We need to automate this for tdmpc and others
-                # ("observation", "image"),
-                ("observation", "state"),
-                # TODO(rcadene): for tdmpc, we might want next image and state
-                # ("next", "observation", "image"),
-                # ("next", "observation", "state"),
-                ("action"),
-            ],
-            mode="min_max",
-        )
-
-        # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
-        transform.stats["observation", "state", "min"] = torch.tensor(
-            [13.456424, 32.938293], dtype=torch.float32
-        )
-        transform.stats["observation", "state", "max"] = torch.tensor(
-            [496.14618, 510.9579], dtype=torch.float32
-        )
-        transform.stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
-        transform.stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
-
-        if writer is None:
-            writer = ImmutableDatasetWriter()
-        if collate_fn is None:
-            collate_fn = _collate_id
-
         super().__init__(
-            storage=storage,
+            dataset_id,
-            sampler=sampler,
+            batch_size,
-            writer=writer,
+            shuffle=shuffle,
-            collate_fn=collate_fn,
+            root=root,
             pin_memory=pin_memory,
             prefetch=prefetch,
-            batch_size=batch_size,
+            sampler=sampler,
+            collate_fn=collate_fn,
+            writer=writer,
             transform=transform,
         )
 
-    @property
-    def num_samples(self) -> int:
-        return len(self)
-
-    @property
-    def num_episodes(self) -> int:
-        return len(self._storage._storage["episode"].unique())
-
-    @property
-    def data_path_root(self) -> Path:
-        return None if self.streaming else self.root / self.dataset_id
-
-    def _is_downloaded(self) -> bool:
-        return self.data_path_root.is_dir()
-
     def _download_and_preproc(self):
-        # download
+        raw_dir = self.data_dir / "raw"
-        raw_dir = self.root / "raw"
         zarr_path = (raw_dir / PUSHT_ZARR).resolve()
         if not zarr_path.is_dir():
             raw_dir.mkdir(parents=True, exist_ok=True)
@@ -266,8 +183,7 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
             # last step of demonstration is considered done
             done[-1] = True
 
-            print("before " + """episode = TensorDict(""")
+            ep_td = TensorDict(
-            episode = TensorDict(
                 {
                     ("observation", "image"): image[:-1],
                     ("observation", "state"): agent_pos[:-1],
@@ -286,120 +202,11 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
 
             if episode_id == 0:
                 # hack to initialize tensordict data structure to store episodes
-                td_data = episode[0].expand(total_frames).memmap_like(self.root / self.dataset_id)
+                td_data = ep_td[0].expand(total_frames).memmap_like(self.data_dir)
 
-            td_data[idxtd : idxtd + len(episode)] = episode
+            td_data[idxtd : idxtd + len(ep_td)] = ep_td
 
             idx0 = idx1
-            idxtd = idxtd + len(episode)
+            idxtd = idxtd + len(ep_td)
 
         return TensorStorage(td_data.lock_())
-
-    def _compute_stats(self, storage, num_batch=100, batch_size=32):
-        rb = TensorDictReplayBuffer(
-            storage=storage,
-            batch_size=batch_size,
-            prefetch=True,
-        )
-        batch = rb.sample()
-
-        image_channels = batch["observation", "image"].shape[1]
-        image_mean = torch.zeros(image_channels)
-        image_std = torch.zeros(image_channels)
-        image_max = torch.tensor([-math.inf] * image_channels)
-        image_min = torch.tensor([math.inf] * image_channels)
-
-        state_channels = batch["observation", "state"].shape[1]
-        state_mean = torch.zeros(state_channels)
-        state_std = torch.zeros(state_channels)
-        state_max = torch.tensor([-math.inf] * state_channels)
-        state_min = torch.tensor([math.inf] * state_channels)
-
-        action_channels = batch["action"].shape[1]
-        action_mean = torch.zeros(action_channels)
-        action_std = torch.zeros(action_channels)
-        action_max = torch.tensor([-math.inf] * action_channels)
-        action_min = torch.tensor([math.inf] * action_channels)
-
-        for _ in tqdm.tqdm(range(num_batch)):
-            image_mean += einops.reduce(batch["observation", "image"], "b c h w -> c", "mean")
-            state_mean += einops.reduce(batch["observation", "state"], "b c -> c", "mean")
-            action_mean += einops.reduce(batch["action"], "b c -> c", "mean")
-
-            b_image_max = einops.reduce(batch["observation", "image"], "b c h w -> c", "max")
-            b_image_min = einops.reduce(batch["observation", "image"], "b c h w -> c", "min")
-            b_state_max = einops.reduce(batch["observation", "state"], "b c -> c", "max")
-            b_state_min = einops.reduce(batch["observation", "state"], "b c -> c", "min")
-            b_action_max = einops.reduce(batch["action"], "b c -> c", "max")
-            b_action_min = einops.reduce(batch["action"], "b c -> c", "min")
-            image_max = torch.maximum(image_max, b_image_max)
-            image_min = torch.maximum(image_min, b_image_min)
-            state_max = torch.maximum(state_max, b_state_max)
-            state_min = torch.maximum(state_min, b_state_min)
-            action_max = torch.maximum(action_max, b_action_max)
-            action_min = torch.maximum(action_min, b_action_min)
-
-            batch = rb.sample()
-
-        image_mean /= num_batch
-        state_mean /= num_batch
-        action_mean /= num_batch
-
-        for i in tqdm.tqdm(range(num_batch)):
-            b_image_mean = einops.reduce(batch["observation", "image"], "b c h w -> c", "mean")
-            b_state_mean = einops.reduce(batch["observation", "state"], "b c -> c", "mean")
-            b_action_mean = einops.reduce(batch["action"], "b c -> c", "mean")
-            image_std += (b_image_mean - image_mean) ** 2
-            state_std += (b_state_mean - state_mean) ** 2
-            action_std += (b_action_mean - action_mean) ** 2
-
-            b_image_max = einops.reduce(batch["observation", "image"], "b c h w -> c", "max")
-            b_image_min = einops.reduce(batch["observation", "image"], "b c h w -> c", "min")
-            b_state_max = einops.reduce(batch["observation", "state"], "b c -> c", "max")
-            b_state_min = einops.reduce(batch["observation", "state"], "b c -> c", "min")
-            b_action_max = einops.reduce(batch["action"], "b c -> c", "max")
-            b_action_min = einops.reduce(batch["action"], "b c -> c", "min")
-            image_max = torch.maximum(image_max, b_image_max)
-            image_min = torch.maximum(image_min, b_image_min)
-            state_max = torch.maximum(state_max, b_state_max)
-            state_min = torch.maximum(state_min, b_state_min)
-            action_max = torch.maximum(action_max, b_action_max)
-            action_min = torch.maximum(action_min, b_action_min)
-
-            if i < num_batch - 1:
-                batch = rb.sample()
-
-        image_std = torch.sqrt(image_std / num_batch)
-        state_std = torch.sqrt(state_std / num_batch)
-        action_std = torch.sqrt(action_std / num_batch)
-
-        stats = TensorDict(
-            {
-                ("observation", "image", "mean"): image_mean[None, :, None, None],
-                ("observation", "image", "std"): image_std[None, :, None, None],
-                ("observation", "image", "max"): image_max[None, :, None, None],
-                ("observation", "image", "min"): image_min[None, :, None, None],
-                ("observation", "state", "mean"): state_mean[None, :],
-                ("observation", "state", "std"): state_std[None, :],
-                ("observation", "state", "max"): state_max[None, :],
-                ("observation", "state", "min"): state_min[None, :],
-                ("action", "mean"): action_mean[None, :],
-                ("action", "std"): action_std[None, :],
-                ("action", "max"): action_max[None, :],
-                ("action", "min"): action_min[None, :],
-            },
-            batch_size=[],
-        )
-        stats["next", "observation", "image"] = stats["observation", "image"]
-        stats["next", "observation", "state"] = stats["observation", "state"]
-        return stats
-
-    def _compute_or_load_stats(self, storage) -> TensorDict:
-        stats_path = self.root / self.dataset_id / "stats.pth"
-        if stats_path.exists():
-            stats = torch.load(stats_path)
-        else:
-            logging.info(f"compute_stats and save to {stats_path}")
-            stats = self._compute_stats(storage)
-            torch.save(stats, stats_path)
-        return stats

lerobot/common/datasets/simxarm.py

@@ -1,4 +1,3 @@
-import os
 import pickle
 from pathlib import Path
 from typing import Callable
@@ -7,130 +6,52 @@ import torch
 import torchrl
 import tqdm
 from tensordict import TensorDict
-from torchrl.data.datasets.utils import _get_root_dir
-from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
 from torchrl.data.replay_buffers.samplers import (
-    Sampler,
     SliceSampler,
-    SliceSamplerWithoutReplacement,
 )
-from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
+from torchrl.data.replay_buffers.storages import TensorStorage
-from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
+from torchrl.data.replay_buffers.writers import Writer
+
+from lerobot.common.datasets.abstract import AbstractExperienceReplay
 
 
-class SimxarmExperienceReplay(TensorDictReplayBuffer):
+class SimxarmExperienceReplay(AbstractExperienceReplay):
     available_datasets = [
         "xarm_lift_medium",
     ]
 
     def __init__(
         self,
-        dataset_id,
+        dataset_id: str,
         batch_size: int = None,
         *,
         shuffle: bool = True,
-        num_slices: int = None,
-        slice_len: int = None,
-        pad: float = None,
-        replacement: bool = None,
-        streaming: bool = False,
         root: Path = None,
-        download: bool = False,
-        sampler: Sampler = None,
-        writer: Writer = None,
-        collate_fn: Callable = None,
         pin_memory: bool = False,
         prefetch: int = None,
-        transform: "torchrl.envs.Transform" = None,  # noqa-F821
+        sampler: SliceSampler = None,
-        split_trajs: bool = False,
+        collate_fn: Callable = None,
-        strict_length: bool = True,
+        writer: Writer = None,
+        transform: "torchrl.envs.Transform" = None,
     ):
-        self.download = download
-        if streaming:
-            raise NotImplementedError
-        self.streaming = streaming
-        self.dataset_id = dataset_id
-        self.split_trajs = split_trajs
-        self.shuffle = shuffle
-        self.num_slices = num_slices
-        self.slice_len = slice_len
-        self.pad = pad
-
-        self.strict_length = strict_length
-        if (self.num_slices is not None) and (self.slice_len is not None):
-            raise ValueError("num_slices or slice_len can be not None, but not both.")
-        if split_trajs:
-            raise NotImplementedError
-
-        if root is None:
-            root = _get_root_dir("simxarm")
-            os.makedirs(root, exist_ok=True)
-        self.root = Path(root)
-        if self.download == "force" or (self.download and not self._is_downloaded()):
-            storage = self._download_and_preproc()
-        else:
-            storage = TensorStorage(TensorDict.load_memmap(self.root / dataset_id))
-
-        if num_slices is not None or slice_len is not None:
-            if sampler is not None:
-                raise ValueError("`num_slices` and `slice_len` are exclusive with the `sampler` argument.")
-
-            if replacement:
-                if not self.shuffle:
-                    raise RuntimeError("shuffle=False can only be used when replacement=False.")
-                sampler = SliceSampler(
-                    num_slices=num_slices,
-                    slice_len=slice_len,
-                    strict_length=strict_length,
-                )
-            else:
-                sampler = SliceSamplerWithoutReplacement(
-                    num_slices=num_slices,
-                    slice_len=slice_len,
-                    strict_length=strict_length,
-                    shuffle=self.shuffle,
-                )
-
-        if writer is None:
-            writer = ImmutableDatasetWriter()
-        if collate_fn is None:
-            collate_fn = _collate_id
-
         super().__init__(
-            storage=storage,
+            dataset_id,
-            sampler=sampler,
+            batch_size,
-            writer=writer,
+            shuffle=shuffle,
-            collate_fn=collate_fn,
+            root=root,
             pin_memory=pin_memory,
             prefetch=prefetch,
-            batch_size=batch_size,
+            sampler=sampler,
+            collate_fn=collate_fn,
+            writer=writer,
             transform=transform,
         )
 
-    @property
-    def num_samples(self):
-        return len(self)
-
-    @property
-    def num_episodes(self):
-        return len(self._storage._storage["episode"].unique())
-
-    @property
-    def data_path_root(self):
-        if self.streaming:
-            return None
-        return self.root / self.dataset_id
-
-    def _is_downloaded(self):
-        return os.path.exists(self.data_path_root)
-
     def _download_and_preproc(self):
         # download
         # TODO(rcadene)
 
-        # load
+        dataset_path = self.data_dir / "buffer.pkl"
-        dataset_dir = Path("data") / self.dataset_id
-        dataset_path = dataset_dir / "buffer.pkl"
         print(f"Using offline dataset '{dataset_path}'")
         with open(dataset_path, "rb") as f:
             dataset_dict = pickle.load(f)
@@ -172,7 +93,7 @@ class SimxarmExperienceReplay(TensorDictReplayBuffer):
 
             if episode_id == 0:
                 # hack to initialize tensordict data structure to store episodes
-                td_data = episode[0].expand(total_frames).memmap_like(self.root / self.dataset_id)
+                td_data = episode[0].expand(total_frames).memmap_like(self.data_dir)
 
             td_data[idx0:idx1] = episode
 

lerobot/common/logger.py

@@ -6,6 +6,10 @@ from omegaconf import OmegaConf
 from termcolor import colored
 
 
+def log_output_dir(out_dir):
+    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
+
+
 def cfg_to_group(cfg, return_list=False):
     """Return a wandb-safe group name for logging. Optionally returns group name as list."""
     # lst = [cfg.task, cfg.modality, re.sub("[^0-9a-zA-Z]+", "-", cfg.exp_name)]

lerobot/scripts/eval.py

@@ -9,13 +9,13 @@ import numpy as np
 import torch
 import tqdm
 from tensordict.nn import TensorDictModule
-from termcolor import colored
 from torchrl.envs import EnvBase
 
 from lerobot.common.datasets.factory import make_offline_buffer
 from lerobot.common.envs.factory import make_env
+from lerobot.common.logger import log_output_dir
 from lerobot.common.policies.factory import make_policy
-from lerobot.common.utils import set_seed
+from lerobot.common.utils import init_logging, set_seed
 
 
 def write_video(video_path, stacked_frames, fps):
@@ -109,10 +109,18 @@ def eval(cfg: dict, out_dir=None):
     if out_dir is None:
         raise NotImplementedError()
 
-    assert torch.cuda.is_available()
+    init_logging()
+
+    if cfg.device == "cuda":
+        assert torch.cuda.is_available()
+    else:
+        logging.warning("Using CPU, this will be slow.")
+
     torch.backends.cudnn.benchmark = True
+    torch.backends.cuda.matmul.allow_tf32 = True
     set_seed(cfg.seed)
-    print(colored("Log dir:", "yellow", attrs=["bold"]), out_dir)
+
+    log_output_dir(out_dir)
 
     logging.info("make_offline_buffer")
     offline_buffer = make_offline_buffer(cfg)
@@ -142,6 +150,8 @@ def eval(cfg: dict, out_dir=None):
     )
     print(metrics)
 
+    logging.info("End of eval")
+
 
 if __name__ == "__main__":
     eval_cli()

lerobot/scripts/train.py

@@ -4,13 +4,12 @@ import hydra
 import numpy as np
 import torch
 from tensordict.nn import TensorDictModule
-from termcolor import colored
 from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
 from torchrl.data.replay_buffers import PrioritizedSliceSampler
 
 from lerobot.common.datasets.factory import make_offline_buffer
 from lerobot.common.envs.factory import make_env
-from lerobot.common.logger import Logger
+from lerobot.common.logger import Logger, log_output_dir
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.utils import format_big_number, init_logging, set_seed
 from lerobot.scripts.eval import eval_policy
@@ -164,7 +163,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
     # log metrics to terminal and wandb
     logger = Logger(out_dir, job_name, cfg)
 
-    logging.info(colored("Work dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
+    log_output_dir(out_dir)
     logging.info(f"{cfg.env.task=}")
     logging.info(f"{cfg.offline_steps=} ({format_big_number(cfg.offline_steps)})")
     logging.info(f"{cfg.online_steps=}")
@@ -212,7 +211,6 @@ def train(cfg: dict, out_dir=None, job_name=None):
     for env_step in range(cfg.online_steps):
         if env_step == 0:
             logging.info("Start online training by interacting with environment")
-        # TODO: use SyncDataCollector for that?
         # TODO: add configurable number of rollout? (default=1)
         with torch.no_grad():
             rollout = env.rollout(
@@ -268,6 +266,8 @@ def train(cfg: dict, out_dir=None, job_name=None):
             step += 1
             online_step += 1
 
+    logging.info("End of training")
+
 
 if __name__ == "__main__":
     train_cli()

lerobot/scripts/visualize_dataset.py

@@ -1,13 +1,20 @@
+import logging
+import threading
 from pathlib import Path
 
+import einops
 import hydra
 import imageio
 import torch
-from torchrl.data.replay_buffers import SliceSamplerWithoutReplacement
+from torchrl.data.replay_buffers import (
+    SamplerWithoutReplacement,
+)
 
 from lerobot.common.datasets.factory import make_offline_buffer
+from lerobot.common.logger import log_output_dir
+from lerobot.common.utils import init_logging
 
-NUM_EPISODES_TO_RENDER = 10
+NUM_EPISODES_TO_RENDER = 50
 MAX_NUM_STEPS = 1000
 FIRST_FRAME = 0
 
@@ -17,45 +24,88 @@ def visualize_dataset_cli(cfg: dict):
     visualize_dataset(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)
 
 
+def cat_and_write_video(video_path, frames, fps):
+    frames = torch.cat(frames)
+    assert frames.dtype == torch.uint8
+    frames = einops.rearrange(frames, "b c h w -> b h w c").numpy()
+    imageio.mimsave(video_path, frames, fps=fps)
+
+
 def visualize_dataset(cfg: dict, out_dir=None):
     if out_dir is None:
         raise NotImplementedError()
 
-    sampler = SliceSamplerWithoutReplacement(
+    init_logging()
-        num_slices=1,
+    log_output_dir(out_dir)
-        strict_length=False,
+
+    # we expect frames of each episode to be stored next to each others sequentially
+    sampler = SamplerWithoutReplacement(
         shuffle=False,
     )
 
-    offline_buffer = make_offline_buffer(cfg, sampler)
+    logging.info("make_offline_buffer")
+    offline_buffer = make_offline_buffer(
+        cfg, overwrite_sampler=sampler, normalize=False, overwrite_batch_size=1, overwrite_prefetch=12
+    )
 
-    for _ in range(NUM_EPISODES_TO_RENDER):
+    logging.info("Start rendering episodes from offline buffer")
-        episode = offline_buffer.sample(MAX_NUM_STEPS)
 
-        ep_idx = episode["episode"][FIRST_FRAME].item()
+    threads = []
-        ep_frames = torch.cat(
+    frames = {}
-            [
+    current_ep_idx = 0
-                episode["observation"]["image"][FIRST_FRAME][None, ...],
+    logging.info(f"Visualizing episode {current_ep_idx}")
-                episode["next", "observation"]["image"],
+    for _ in range(MAX_NUM_STEPS * NUM_EPISODES_TO_RENDER):
-            ],
+        # TODO(rcadene): make it work with bsize > 1
-            dim=0,
+        ep_td = offline_buffer.sample(1)
-        )
+        ep_idx = ep_td["episode"][FIRST_FRAME].item()
 
-        video_dir = Path(out_dir) / "visualize_dataset"
+        # TODO(rcaene): modify offline_buffer._sampler._sample_list or sampler to randomly sample an episode, but sequentially sample frames
-        video_dir.mkdir(parents=True, exist_ok=True)
+        no_more_frames = offline_buffer._sampler._sample_list.numel() == 0
-        # TODO(rcadene): make fps configurable
+        new_episode = ep_idx != current_ep_idx
-        video_path = video_dir / f"episode_{ep_idx}.mp4"
 
-        assert ep_frames.min().item() >= 0
+        if new_episode:
-        assert ep_frames.max().item() > 1, "Not mendatory, but sanity check"
+            logging.info(f"Visualizing episode {current_ep_idx}")
-        assert ep_frames.max().item() <= 255
-        ep_frames = ep_frames.type(torch.uint8)
-        imageio.mimsave(video_path, ep_frames.numpy().transpose(0, 2, 3, 1), fps=cfg.fps)
 
-        # ran out of episodes
+        for im_key in offline_buffer.image_keys:
-        if offline_buffer._sampler._sample_list.numel() == 0:
+            if new_episode or no_more_frames:
+                # append last observed frames (the ones after last action taken)
+                frames[im_key].append(ep_td[("next", *im_key)])
+
+                video_dir = Path(out_dir) / "visualize_dataset"
+                video_dir.mkdir(parents=True, exist_ok=True)
+
+                if len(offline_buffer.image_keys) > 1:
+                    camera = im_key[-1]
+                    video_path = video_dir / f"episode_{current_ep_idx}_{camera}.mp4"
+                else:
+                    video_path = video_dir / f"episode_{current_ep_idx}.mp4"
+
+                thread = threading.Thread(
+                    target=cat_and_write_video,
+                    args=(str(video_path), frames[im_key], cfg.fps),
+                )
+                thread.start()
+                threads.append(thread)
+
+                current_ep_idx = ep_idx
+
+                # reset list of frames
+                del frames[im_key]
+
+            # append current cameras images to list of frames
+            if im_key not in frames:
+                frames[im_key] = []
+            frames[im_key].append(ep_td[im_key])
+
+        if no_more_frames:
+            logging.info("Ran out of frames")
             break
 
+    for thread in threads:
+        thread.join()
+
+    logging.info("End of visualize_dataset")
+
 
 if __name__ == "__main__":
     visualize_dataset_cli()